Perturbation experiments for example using RNA interference (RNAi) offer an attractive way to elucidate gene function in a high throughput fashion. The placement of hit genes in their functional context and the inference of underlying networks from such data, however, are challenging tasks. One of the problems in network inference is the exponential number of possible network topologies for a given number of genes. Here, we introduce a novel mathematical approach to address this question. We formulate network inference as a linear optimization problem, which can be solved efficiently even for large-scale systems. We use simulated data to evaluate our approach, and show improved performance in particular on larger networks over state-of-the art methods. We achieve increased sensitivity and specificity, as well as a significant reduction in computing time. Furthermore, we show superior performance on noisy data. We then apply our approach to study the intracellular signaling of human primary nave CD4+ T-cells, as well as ErbB signaling in trastuzumab resistant breast cancer cells. In both cases, our approach recovers known interactions and points to additional relevant processes. In ErbB signaling, our results predict an important role of negative and positive feedback in controlling the cell cycle progression.

L’acétylation est une modification post-traductionnelle des protéines essentielles. Elle est impliquée dans bon nombre de processus cellulaires importants comme la régulation de la structure de la chromatine et le recrutement de protéines. Deux groupes d’enzymes, soient les lysines acétyltransférases et les lysines désacétylases, régulent cette modification, autant sur les histones que sur les autres protéines. Au cours des dernières années, de petites molécules inhibitrices des désacétylases ont été découvertes. Certaines d’entre elles semblent prometteuses contre diverses maladies telles le cancer. L’acide valproïque, un inhibiteur de deux des trois classes des désacétylases, a un effet antiprolifératif chez plusieurs organismes modèles. Toutefois, les mécanismes cellulaires sous-jacents à cet effet restent encore méconnus. Ce mémoire met en lumière l’effet pH dépendant de l’acide valproïque sur différentes voies cellulaires importantes chez la levure Saccharomyces cerevisiae. Il démontre que ce composé a la capacité d’inhiber la transition entre les phases G1 et S par son action sur l’expression des cyclines de la phase G1. De plus, il inhibe l’activation de la kinase principale de la voie activée suite à un stress à la paroi cellulaire. L’acide valproïque occasionne également un arrêt dans la réplication de l’ADN sans y causer de dommage. Il s’agit là d’un effet unique qui, à notre connaissance, n’est pas observable avec d’autres agents qui inhibent la progression en phase S. / Acetylation is an essential post-translational modification involved in many important cellular processes such as regulation of chromatin structure and proteins interactions. Two enzyme families, lysine acetyltransferases and lysine deacetylases, allow proper regulation of this modification both on histones and non-histones proteins. In recent years, the discovery of small deacetylase inhibitors has led to promising novel therapy in the treatment against various diseases such as cancer. Valproic acid, a class I and II deacetylase inhibitor, has been shown to have antiproliferative effects in various models. However, the cellular mechanisms underlying this effect remain unknown. This thesis highlights the pH-dependent effects of VPA on numerous important cellular pathways in the yeast Saccharomyces cerevisiae. Our results demonstrate that VPA inhibits the transition from G1 to S phase of the cell cycle by its action on the expression of G1 cyclins. Moreover, VPA inhibits the activation of the main kinase involved in the cell wall integrity pathway. Furthermore, VPA exposure also leads to DNA replication arrest in a DNA damage-independent manner. This is a unique effect that, to our knowledge, is not observable with other agents that inhibit S phase progression.